Assessing team strategy using spatiotemporal data

Abstract

The Moneyball revolution coincided with a shift in the way professional sporting organizations handle and utilize data in terms of decision making processes. Due to the demand for better sports analytics and the improvement in sensor technology, there has been a plethora of ball and player tracking information generated within professional sports for analytical purposes. However, due to the continuous nature of the data and the lack of associated high-level labels to describe it -this rich set of information has had very limited use especially in the analysis of a team's tactics and strategy. In this paper, we give an overview of the types of analysis currently performed mostly with hand-labeled event data and highlight the problems associated with the influx of spatiotemporal data. By way of example, we present an approach which uses an entire season of ball tracking data from the English Premier League (2010-2011 season) to reinforce the common held belief that teams should aim to "win home games and draw away ones". We do this by: i) forming a representation of team behavior by chunking the incoming spatiotemporal signal into a series of quantized bins, and ii) generate an expectation model of team behavior based on a code-book of past performances. We show that home advantage in soccer is partly due to the conservative strategy of the away team. We also show that our approach can flag anomalous team behavior which has many potential applications.

Automatic behavior analysis in tag games: from traditional spaces to interactive playgrounds

“…Game settings can vary greatly, from professional sport scenarios, where automatic behavior analysis has been used to aid in understanding team strategies [8,26], to playground games such as tag, Marco Polo or hide-and-seek, where automatic recognition has been used to monitor children's social skills and diagnose social conditions such as autism [28,35,38]. At their most basic level, games require some level of physical exertion (e.g.…”

“…Lucey et al track the ball instead of the players in soccer matches [26]. They estimate the amount of ball possession a team has accumulated in any given part of the court to recognize home and away behavior for teams.…”

Automatically Discovering Offensive Patterns in Soccer Match Data

“…The amount of available data about various sports is constantly increasing, most importantly tracking data and event data [14]. Within soccer, the analysis of tracking data focuses on discovering individual or collective movement patterns, e.g., spectral clustering of trajectories [6], strategy analysis with occupancy maps [12], or formation analysis via minimum entropy partitioning [1]. Gyarmati et al use event data to discover motif patterns in pass sequences [4].…”

From traditional to interactive playspaces: automatic analysis of player behavior in the interactive tag playground

“…Game settings can vary greatly, from professional sport scenarios, where automatic behavior analysis has been used to aid in understanding team strategies [134,135], to playground/children's games such as tag or peek-a-boo, where roles or actions have been automatically detected [136,137]. For instance, Rehg et al used Kinect sensors and microphones to analyze dyadic interactions between adults and 1-2 years old children during simple children games [138].…”

“…Lucey et al tracked the ball instead of the players in soccer matches [135]. They estimated the amount of ball possession a team has accumulated in any given part of the court to recognize home and away behavior for teams.…”